用分割感知的潜空间扩散模型，实现20倍超分辨率，精准划分小农户农田边界。
Segmentation-Aware Latent Diffusion for Satellite Image Super-Resolution: Enabling Smallholder Farm Boundary Delineation
- 在分割感知的潜空间中进行超分辨率，跳过传统像素级重建。
- 在真实数据集上实现20倍放大，实例和语义分割指标分别提升25.5%和12.9%。
- 适合需要高精度农田边界提取的农业遥感应用，尤其适用于低频高清影像补全。
通过分割卫星图像来划定农田边界是众多农业应用的基础步骤。该任务对小农户农田尤为挑战，因精确划定需依赖高分辨率（HR）影像，而这类影像仅能每年获取一次。为支持更频繁的（亚）季节性监测，可将高分辨率影像作为参考（ref），与高重访频率的低分辨率（LR）影像（如每周）结合，使用基于参考的超分辨率（Ref-SR）方法。然而，现有Ref-SR方法优化感知质量，会模糊下游任务所需的关键特征，且难以满足该任务的大尺度因子需求。此外，传统的先超分后分割两步法未能有效利用多源卫星数据。我们提出新型方法SEED-SR，结合条件潜空间扩散模型与大规模多光谱、多源地理空间基础模型。关键创新在于绕过像素空间的显式超分辨率，转而在分割感知的潜空间中执行。该方法可实现前所未有的20×尺度因子，并在两个大型真实数据集上的实验表明，相比现有最优的Ref-SR方法，实例和语义分割指标分别提升25.5%和12.9%。
原文摘要 · Abstract (English)
Delineating farm boundaries through segmentation of satellite images is a fundamental step in many agricultural applications. The task is particularly challenging for smallholder farms, where accurate delineation requires the use of high resolution (HR) imagery which are available only at low revisit frequencies (e.g., annually). To support more frequent (sub-) seasonal monitoring, HR images could be combined as references (ref) with low resolution (LR) images -- having higher revisit frequency (e.g., weekly) -- using reference-based super-resolution (Ref-SR) methods. However, current Ref-SR methods optimize perceptual quality and smooth over crucial features needed for downstream tasks, and are unable to meet the large scale-factor requirements for this task. Further, previous two-step approaches of SR followed by segmentation do not effectively utilize diverse satellite sources as inputs. We address these problems through a new approach, $\textbf{SEED-SR}$, which uses a combination of conditional latent diffusion models and large-scale multi-spectral, multi-source geo-spatial foundation models. Our key innovation is to bypass the explicit SR task in the pixel space and instead perform SR in a segmentation-aware latent space. This unique approach enables us to generate segmentation maps at an unprecedented 20$\times$ scale factor, and rigorous experiments on two large, real datasets demonstrate up to $\textbf{25.5}$ and $\textbf{12.9}$ relative improvement in instance and semantic segmentation metrics respectively over approaches based on state-of-the-art Ref-SR methods.
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